Fallahzadeh, RaminPagán Ortiz, JosuéGhasemzadeh, Hassan2024-01-232024-01-232017R. Fallahzadeh, J. P. Ortiz and H. Ghasemzadeh, "Adaptive compressed sensing at the fingertip of Internet-of-Things sensors: An ultra-low power activity recognition," Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017, Lausanne, Switzerland, 2017, pp. 996-1001, doi: 10.23919/DATE.2017.7927136.10.23919/date.2017.7927136https://hdl.handle.net/20.500.14352/94862With the proliferation of wearable devices in the Internet-of-Things applications, designing highly power-efficient solutions for continuous operation of these technologies in life-critical settings emerges. We propose a novel ultra-low power framework for adaptive compressed sensing in activity recognition. The proposed design uses a coarse-grained activity recognition module to adaptively tune the compressed sensing module for minimized sensing/transmission costs. We pose an optimization problem to minimize activity-specific sensing rates and introduce a polynomial time approximation algorithm using a novel heuristic dynamic optimization tree. Our evaluations on real-world data shows that the proposed autonomous framework is capable of generating feedback with -80% confidence and improves power reduction performance of the state-of-the-art approach by a factor of two.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/conference paper1558-1101https://www.doi.org/10.23919/date.2017.7927136https://ieeexplore.ieee.org/document/7927136restricted accessElectrónica (Informática)3307 Tecnología Electrónica